Well-posedness and discretization for a class of models for mixed-dimensional problems with high-dimensional gap

In this work, we show the underlying mathematical structure of mixed-dimensional models arising from the composition of graphs and continuous domains. Such models are becoming popular in applications, in particular, to model the human vasculature. We first discuss the model equations in the strong form, which describes the conservation of mass and Darcy's law in the continuum and network as well as the coupling between them. By introducing proper scaling, we propose a weak form that avoids degeneracy. Well-posedness of the weak form is shown through standard Babu\v ska--Brezzi theory. We also develop the mixed formulation finite-element method and prove its well-posedness. A mass-lumping technique is introduced to derive the two-point flux approximation (TPFA) type discretization as well, due to its importance in applications. Based on the Babu\v ska--Brezzi theory, error estimates can be obtained for both the finite-element scheme and the TPFA scheme. We also discuss efficient linear solvers for discrete problems. Finally, we present some numerical examples to verify the theoretical results and demonstrate the robustness of our proposed discretization schemes.acceptedVersio

Modeling a 3-D multiscale blood-flow and heat-transfer framework for realistic vascular systems

Modeling of biological domains and simulation of biophysical processes occurring in them can help inform medical procedures. However, when considering complex domains such as large regions of the human body, the complexities of blood vessel branching and variation of blood vessel dimensions present a major modeling challenge. Here, we present a Voxelized Multi-Physics Simulation (VoM-PhyS) framework to simulate coupled heat transfer and fluid flow using a multi-scale voxel mesh on a biological domain obtained. In this framework, flow in larger blood vessels is modeled using the Hagen–Poiseuille equation for a one-dimensional flow coupled with a three-dimensional two-compartment porous media model for capillary circulation in tissue. The Dirac distribution function is used as Sphere of Influence (SoI) parameter to couple the one-dimensional and three-dimensional flow. This blood flow system is coupled with a heat transfer solver to provide a complete thermo-physiological simulation. The framework is demonstrated on a frog tongue and further analysis is conducted to study the effect of convective heat exchange between blood vessels and tissue, and the effect of SoI on simulation results.publishedVersio

Fractional anisotropy shows differential reduction in frontal-subcortical fiber bundles - A longitudinal MRI study of 76 middle-aged and older adults

Motivated by the frontal- and white matter (WM) retrogenesis hypotheses and the assumptions that fronto-striatal circuits are especially vulnerable in normal aging, the goal of the present study was to identify fiber bundles connecting subcortical nuclei and frontal areas and obtain site-specific information about age related fractional anisotropy (FA) changes. Multimodal magnetic resonance image acquisitions [3D T1-weighted and diffusion weighted imaging (DWI)] were obtained from healthy older adults (N = 76, range 49–80 years at inclusion) at two time points, 3 years apart. A subset of the participants (N = 24) was included at a third time-point. In addition to the frontal-subcortical fibers, the anterior callosal fiber (ACF) and the corticospinal tract (CST) was investigated by its mean FA together with tract parameterization analysis. Our results demonstrated fronto-striatal structural connectivity decline (reduced FA) in normal aging with substantial inter-individual differences. The tract parameterization analysis showed that the along tract FA profiles were characterized by piece-wise differential changes along their extension rather than being uniformly affected. To the best of our knowledge, this is the first longitudinal study detecting age-related changes in frontal-subcortical WM connections in normal aging.publishedVersio

Partitioning and Exocytosis of Secretory Granules during Division of PC12 Cells

The biogenesis, maturation, and exocytosis of secretory granules in interphase cells have been well documented, whereas the distribution and exocytosis of these hormone-storing organelles during cell division have received little attention. By combining ultrastructural analyses and time-lapse microscopy, we here show that, in dividing PC12 cells, the prominent peripheral localization of secretory granules is retained during prophase but clearly reduced during prometaphase, ending up with only few peripherally localized secretory granules in metaphase cells. During anaphase and telophase, secretory granules exhibited a pronounced movement towards the cell midzone and, evidently, their tracks colocalized with spindle microtubules. During cytokinesis, secretory granules were excluded from the midbody and accumulated at the bases of the intercellular bridge. Furthermore, by measuring exocytosis at the single granule level, we showed, that during all stages of cell division, secretory granules were competent for regulated exocytosis. In conclusion, our data shed new light on the complex molecular machinery of secretory granule redistribution during cell division, which facilitates their release from the F-actin-rich cortex and active transport along spindle microtubules

Interactive Visual Analysis of Heterogeneous Cohort Study Data

Cohort studies in medicine are conducted to enable the study of medical hypotheses in large samples. Often, a large amount of heterogeneous data is acquired from many subjects. The analysis is usually hypothesis-driven, i.e., a specific subset of such data is studied to confirm or reject specific hypotheses. In this paper, we demonstrate how we enable the interactive visual exploration and analysis of such data, helping with the generation of new hypotheses and contributing to the process of validating them. We propose a data-cube based model which handles partially overlapping data subsets during the interactive visualization. This model enables seamless integration of the heterogeneous data, as well as linking spatial and non-spatial views on these data. We implemented this model in an application prototype, and used it to analyze data acquired in the context of a cohort study on cognitive aging. We present case-study analyses of selected aspects of brain connectivity by using the prototype implementation of the presented model, to demonstrate its potential and flexibility

A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model

A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.publishedVersio

A radiogenomics application for prognostic profiling of endometrial cancer

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.publishedVersio

Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer

Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (n = 105) and a test- (n = 26) cohort. The segmentation performance of the DL algorithm compared with R1/R2 was assessed with Dice coefficients (DSCs) and Hausdorff distances (HDs) in the test cohort. The trained DL network retrieved whole-volume tumor segmentations yielding median DSCs of 0.60 and 0.58 for DL compared with R1 (DL-R1) and R2 (DL-R2), respectively, whereas DSC for R1-R2 was 0.78. Agreement for primary tumor volumes was excellent between raters (R1-R2: intraclass correlation coefficient (ICC) = 0.93), but lower for the DL algorithm and the raters (DL-R1: ICC = 0.43; DL-R2: ICC = 0.44). The developed DL algorithm enables the automated estimation of tumor size and primary CC tumor segmentation. However, segmentation agreement between raters is better than that between DL algorithm and raters.publishedVersio

Rab3D is critical for secretory granule maturation in PC12 cells.

Neuropeptide- and hormone-containing secretory granules (SGs) are synthesized at the trans-Golgi network (TGN) as immature secretory granules (ISGs) and complete their maturation in the F-actin-rich cell cortex. This maturation process is characterized by acidification-dependent processing of cargo proteins, condensation of the SG matrix and removal of membrane and proteins not destined to mature secretory granules (MSGs). Here we addressed a potential role of Rab3 isoforms in these maturation steps by expressing their nucleotide-binding deficient mutants in PC12 cells. Our data show that the presence of Rab3D(N135I) decreases the restriction of maturing SGs to the F-actin-rich cell cortex, blocks the removal of the endoprotease furin from SGs and impedes the processing of the luminal SG protein secretogranin II. This strongly suggests that Rab3D is implicated in the subcellular localization and maturation of ISGs

In thrombin stimulated human platelets Citalopram, Promethazine, Risperidone, and Ziprasidone, but not Diazepam, may exert their pharmacological effects also through intercalation in membrane phospholipids in a receptor-independent manner

Intercalation of drugs in the platelet membrane affects phospholipid-requiring enzymatic processes according to the drugs’ intercalation capability. We investigated effects of Promethazine, Citalopram, Ziprasidone, Risperidone, and Diazepam on phospholipase A2 (PLA2) and polyphosphoinositide (PPI) metabolism in thrombin-stimulated human platelets. We also examined effects of the drugs on monolayers of glycerophospholipids using the Langmuir technique. Diazepam did not influence PLA2 activity, had no effects on PPI cycle, and caused no change in mean molecular area of phospholipid monolayers. The remaining psychotropic drugs affected these parameters in different ways and levels of potency suggesting that they act by being intercalated between the molecules of adjacent membrane phospholipids, thus causing changes in substrate availability for phospholipid-hydrolyzing enzymes (PLA2 and Phospholipase C). We show that several psychotropic drugs can also have other cellular effects than receptor antagonism. These effects may be implicated in the psychotropic effects of the drugs and/or their side effects